Abstract The control and power production of a Doubly Fed Induction Generator (DFIG) wind turbine are highly dependent on grid parameters. To reach the maximum power point (MPPT) and hence to maximize the power output from the wind energy system, the MPPT algorithms used are generally dependent on the grid voltage measurements. Therefore, any deviation of the grid voltage from its rated value can reduce both the power production efficiency and the reactive power performance of the wind turbine. The main sources of these deviations are voltage unbalance and grid disturbances. Deep investigations of a year of data from the Nouadhibou wind farm located in Mauritania, have shown that DFIG wind turbines are very sensitive to the operating conditions defined by the grid code. This grid code specifies two operation modes: unlimited temporal operation and limited operation. In many situations, the wind farm exceeds the allowed time for limited operation, leading to grid disconnection. This paper proposes a new reactive power control strategy to reduce DFIG’s sensitivity to grid voltage variations. The proposed control compares the measured Phase-Locked Loop (PLL) signal with the reference voltage. Based on this comparison, the controller chooses the suitable combination of capacitors to generate or absorb the required reactive power for voltage regulation. This method allows the wind turbine to remain in unlimited operation mode, preventing unwanted disconnections. Furthermore, it reduces reactive power exchange between the DFIG and the grid while keeping the Point of Common Coupling (PCC) voltage within optimal limits.

Abstract Recent advancements in decentralised renewable energy offer solutions for reliable grid-based power in remote Indian regions. This study introduces an AHDES (autonomous hybrid distributed energy system) for rural electrification, integrating solar, wind, biomass, and battery storage. A DFIG converts wind energy, with PV panels as the primary source, supported by a biomass generator and battery bank. An SPWM inverter ensures constant voltage and frequency at the PCC, replacing the conventional back-to-back converter for rotor power supply. Converter output current-based maximum power point tracking (COCB-MPPT) is anticipated. This process exploits the relationship between the converter output current and duty ratio to routinely fine-tune step fluctuations, thus maximising power output from the PV panel. In this study, a VFCL (voltage and frequency closed loop) controller combined with a harmonic filter is employed as a voltage controller to reduce the THD percentage. The AHDES topology is simulated in MATLAB and experimentally validated under various operating conditions. Test results presented indicate that the VFCL control strategy effectively maintains the stator voltage at 415 V and the frequency at 50 Hz under all conditions. Further, measured THD values remain below 3 % for voltage and below 6.5 % for current. This demonstrates that the hybrid Solar PV-wind-biomass AHDES maintains stability. Furthermore, the results confirm that the VFCL controller with a harmonic filter is an effective approach for mitigating voltage instability.

Abstract Image monitoring and transmission systems widely used in smart grids face problems such as complex electromagnetic interference, privacy leakage and inefficient retrieval. To address these challenges, this paper proposes an anti-interference transmission algorithm for sensitive images of smart grid based on the combination of Tent chaotic mapping and deep learning. The method improves NSGA-II optimization algorithm through Tent chaotic mapping to achieve global convergence enhancement and local optimal jumping out ability; meanwhile, it integrates DenseNet deep features with traditional HOG, BOW, ColorSpace features, and adopts PCA dimensionality reduction to improve the anti-interference expression ability of the features. In order to safeguard privacy security and retrieval efficiency, this paper designs a fine-grained access control based on CP-ABE and ρ -stable LSH secure indexing mechanism to support efficient similarity retrieval in ciphertext state. The experimental results show that the method can significantly reduce the BER, stabilize the DC bus voltage, maintain the fast recovery of three-phase current and voltage waveforms, and outperform the existing schemes in terms of image retrieval accuracy and computational efficiency under the extreme interference conditions, such as high-voltage overlay discharge and power switching. This study provides a new theoretical support and engineering realization path for the safe and efficient transmission of sensitive images in smart grids.

Abstract The present research motivates the enhancement of power quality in a PV fed DSTATCOM employed in a local distribution network. As per the power quality demand from the consumer, regulation of power has become major focus in presence of renewable systems in the distribution network. The distribution network majorly shows resistance towards the severe uncertainties of the grid and load variations. Therefore, the present research proposes hybrid DSTATCOM for the distribution system with various uncertain load conditions. To enhance the performance of proposed system, a robust SRF controller has been implemented for the generation of reference signal for VSI of the DSTATCOM along with DC offset error compensation (DOEC) based phase-locked loops. In the Hybrid DSTATCOM, regulation of dc-link voltage also plays significant position for the performance of the system. Therefore, in this research paper, bat optimization based MPPT has been implemented for the regulation of dc link voltage at the desired level. The system has been developed in the Matlab/Simulink and results have been analyzed. The system has been also validated under experimental conditions in the laboratory, which shows the systems efficiency.

Abstract State estimation (SE) uses available measurements to extract and analyze information about the power system and support its operation and control. However, its reliability may be compromised through carefully designed False Data Injection (FDI) attacks that target its input measurements. Most existing literature on FDI attacks mounted against SE focus on its approximate, linearized model. Conversely, practical applications often require the use of non-linear SE. This paper proposes a novel approach for the design of FDI attacks against non-linear SE. The proposed approach utilizes the Cartesian formulation of power flow equations to linearize the attack problem, without compromising its accuracy. Two motivating examples using widely adopted test networks are used to demonstrate the effectiveness of the approach. The approach is flexible and can be applied to any network topology and incorporated to different attack design and mitigation studies. It also requires less resources to perform a successful attack as compared to the other relevant approaches proposed in the open literature. This may have important implications on studies that assess the vulnerability of the power system to FDI attacks or propose defense strategies against them, as more resources may be required to implement mitigation techniques than currently assumed in the literature.

Abstract A novel hybrid cascaded H-bridge (CHB) inverter topology is proposed for renewable energy applications, featuring enhanced fault tolerance. By integrating a nine-level boost switched-capacitor module with H-bridge modules, the design ensures reliable operation under faults affecting semiconductor switches or DC power supplies. The topology offers additional benefits, including voltage boosting of four and self-balancing of switched capacitors. Simulation and experimental results validate the proposed inverter’s performance under various operating conditions, demonstrating its potential for robust and efficient renewable energy systems.

Abstract The harsh operating conditions of deep offshore wind farm (OWF) significantly reduce the reliability of electrical collection system (ECS). Traditional assessment methods for conventional AC-ECS often rely on reliability parameters that are unsuitable for the challenging wind conditions encountered in deep offshore environments. Moreover, the lack of empirical data from operational DC-ECS introduces substantial uncertainty into the reliability evaluations. To address these issues, this paper proposes a hierarchical assessment framework, encompassing component, device and system levels. For the key components of DC-ECS that are impacted by harsh wind conditions, the framework begins with multi-time scale lifespan prediction of power modules at the component level. These predictions are then utilized in device-level reliability calculations for critical equipment. Finally, the sequential Markov Chain Monte Carlo (MCMC) method is employed to assess the reliability of ECS under different topologies. A 300 MW wind farm is used as a case study to validate the feasibility of the proposed method, and the influence of individual equipment reliability and other factors on the overall system reliability is analyzed.

Abstract The increasing complexity of modern power distribution systems, driven by renewable integration, evolving load patterns, and aging infrastructure, has accentuated the need for advanced fault detection and classification mechanisms, particularly in emerging medium-voltage (MV) networks such as Moroccan distribution grid. However, traditional protection schemes, often based on centralized logic and fixed thresholds, tend to underperform in complex or high-impedance fault conditions. Furthermore, global signal features such as RMS or frequency components are insufficient to capture the localized and phase-dependent behavior of faults. These limitations have prompted a growing interest in intelligent, data-driven approaches combining signal processing and machine learning to achieve high-resolution fault diagnosis and improved system reliability. The present study proposes and evaluates an intelligent fault classification framework tailored to MV distribution networks. It explores the comparative performance of three neural architectures and supervised learning classifiers, Multilayer Perceptron (MLP), Support Vector Machine (SVM) and Radial Basis Function Neural Network (RBFNN), applied to both globally extracted features and phase-localized wavelet descriptors. In addition to the baseline classification framework, a novel phase-based analysis method is introduced to enhance diagnostic performance. This method processes each phase and neutral current independently, based on the hypothesis that fault signatures emerge more distinctly when analysed separately. The experimental results demonstrate that neural models can reliably identify fault types across both approaches and underscores the potential of wavelet-enhanced AI models for smart and localized protection in emerging power distribution systems and prove its readiness for real-world deployment in SCADA-integrated protection systems.

Abstract With the large-scale integration of distributed generations (DGs) and the expansion of distribution networks, the complexity of fault location has significantly increased. Traditional fault location methods struggle to meet the demands of modern power systems. This paper explores the impact of distributed generation access on fault location in distribution networks and constructs switching functions and evaluation functions suitable for multi-state distributed generation based on fault characteristic information from automated monitoring devices. To augment the optimization performance of fault localization algorithms, this paper proposes an Improved Black Kite Algorithm (IBKA) that incorporates an Elite Learning Strategy along with Lens Imaging Reverse Learning Strategy and Golden Sine Strategy. This enhances the ability to escape local optima effectively. Simulation results demonstrate that the IBKA exhibits high localization accuracy and fault tolerance in complex scenarios, including single-section faults, multiple-section faults, and Feeder Terminal Unit (FTU) information distortion in active distribution networks, significantly improvs convergence speed and solution efficiency.